Correlating SEM and optical images for wafer noise nuisance identification

ABSTRACT

Disclosed are apparatus and methods for inspecting a semiconductor sample. Locations corresponding to candidate defect events on a semiconductor sample are provided from an optical inspector operable to acquire optical images from which such candidate defect events are detected at their corresponding locations across the sample. High-resolution images are acquired from a high-resolution inspector of the candidate defect events at their corresponding locations on the sample. Each of a set of modelled optical images, which have been modeled from a set of the acquired high-resolution images, is correlated with corresponding ones of a set of the acquired optical images, to identify surface noise events, as shown in the set of high-resolution images, as sources for the corresponding candidate events in the set of acquired optical images. Otherwise, a subsurface event is identified as a likely source for a corresponding candidate defect event.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of prior U.S. ProvisionalApplication No. 62/687,192, filed 19 Jun. 2018 by Qiang Zhang et al.,which application is herein incorporated by reference in its entiretyfor all purposes.

TECHNICAL FIELD OF THE INVENTION

The invention generally relates to the field of wafer inspectionsystems. More particularly the present invention relates to defectdetection using both optical and scanning electron microscope (SEM)images or the like.

BACKGROUND

Generally, the industry of semiconductor manufacturing involves highlycomplex techniques for fabricating integrated circuits usingsemiconductor materials which are layered and patterned onto asubstrate, such as silicon. Due to the large scale of circuitintegration and the decreasing size of semiconductor devices, thefabricated devices have become increasingly sensitive to defects. Thatis, defects which cause faults in the device are becoming increasinglysmaller. The device needs to be generally fault free prior to shipmentto the end users or customers.

Optical inspection of patterned wafers at the deep UV (ultraviolet)wavelength has been the main inspection solution for leading-edgeintegrated circuit (IC) fabrication in critical defect inspection andprocess control. As the IC industry continues to push the wafer patterndesign towards smaller design rule for higher device density and betterperformance, the challenges to find yield-limiting defects in the waferinspection also increases dramatically. Among them, one of the greatestchallenges lies in control of wafer noise nuisances.

Accordingly, there is a continued demand for improved semiconductorwafer inspector systems and techniques.

SUMMARY

The following presents a simplified summary of the disclosure in orderto provide a basic understanding of certain embodiments of theinvention. This summary is not an extensive overview of the disclosureand it does not identify key/critical elements of the invention ordelineate the scope of the invention. Its sole purpose is to presentsome concepts disclosed herein in a simplified form as a prelude to themore detailed description that is presented later.

In one embodiment, a method of inspecting a semiconductor sample isdisclosed. Locations corresponding to a plurality of candidate defectevents on a semiconductor sample are provided from an optical inspectoroperable to acquire a plurality of acquired optical images from whichsuch candidate defect events are detected at their correspondinglocations across the sample. High-resolution images are acquired from ahigh-resolution inspector, such as a scanning electron microscope,operable to acquire such high-resolution images of the plurality ofcandidate defect events at their corresponding locations on the sample.Each of a first set of modelled optical images, which have been modeledfrom a first subset of the acquired high-resolution images, iscorrelated with corresponding ones of a first set of the acquiredoptical images, to identify a plurality of surface noise events, asshown in the first set of high-resolution images, as sources for thecorresponding defect events in the first set of acquired optical images.

In another example embodiment, the correlating of each of the first setof modelled optical images results in identification of the surfacenoise events as sources if the corresponding modelled and acquiredoptical images are substantially identical. In this aspect, each of asecond set of modelled optical images, which have been modeled from asecond subset of the acquired high-resolution images, is correlated witha corresponding one of a second set of the acquired optical images sothat noise events are not identified as sources and, instead, subsurfaceevents are identified as sources for the corresponding defect events inthe second set of acquired optical images.

In another implementation, each candidate event represents a surfacedefect event, one or more noise events, or a subsurface event present onthe sample. In another aspect, prior to correlating the first and secondset of modelled images with their corresponding first and second sets ofacquired optical images, the high-resolution images are analyzed toclassify the candidate events into ambiguous and unambiguous events. Inthis aspect, each high-resolution image in the first and second set ofhigh-resolution images is associated with a classified ambiguous event,and each unambiguous event is a bridge, break, protrusion, or otherknown defect type, wherein the ambiguous events were unclassifiable as aknown defect type.

In specific implementation, a near field (NF) model is trained to modela plurality of NF images from corresponding acquired high-resolutionimages. In this aspect, the NF model is trained with a set of traininghigh-resolution images that correspond to unambiguous and classifiedevents. The first and second sets of modeled optical images are thenmodeled by modeling a plurality of corresponding NF images from thefirst and second sets of acquired high-resolution images using thetrained NF model and modeling the first and second sets of modeledoptical images from the corresponding NF images using an optical toolmodel for the optical inspector. In a further aspect, the NF model isconfigured to simulate light reflected and scattered, with a pluralityof light characteristic parameters, from a wafer pattern, having a setof pattern characteristic parameters, that is represented in thecorresponding high-resolution images, wherein the NF model is trained by(i) inputting the training high-resolution images into the NF model tomodel corresponding training NF images based on the light and patterncharacteristic metrics, (ii) inputting the training NF images that weremodelled from the training high-resolution images into the optical modelto model corresponding training modeled optical images, (iii)correlating the training modeled optical images with their correspondingacquired optical images, and (iv) adjusting the light and patterncharacteristic parameters and repeating the operations for inputting thetraining high-resolution images into the NF model, inputting thetraining NF images into the optical model, and correlating the trainingmodeled optical images until such correlating operation results in amaximum correlation between the training modeled optical images andtheir corresponding acquired optical images.

In another aspect, modeling the first and second sets of modeled opticalimages is performed with respect to the first and second set ofhigh-resolution images after they have been smoothed to remove noiseintroduced by the high-resolution inspector and have been binarized by anormalization process, and the correlating of each of the first andsecond set of modelled optical images with corresponding acquiredoptical images is performed after such first and second modeled opticalimages are down-sampled so that their resolution and/or size are thesame as a resolution and size of the corresponding acquired opticalimages. In yet another aspect, each modelled optical image is shiftedwith respect to its corresponding acquired image by an offset that isdetermined by aligning one of the training modeled optical images with acorresponding one of the acquired images, and the shifting results inone or more noise events in a high-resolution image from the first setof high-resolution images being accurately correlated with acorresponding candidate event in the corresponding acquired opticalimage. In another embodiment, after correlating the first and second setof modelled images, it is determined whether the sample is to beprocessed further with or without repair or discarded based on review ofthe high-resolution images, the classified unambiguous events, and theidentified noise and subsurface events if any.

In an alternative embodiment, the invention pertains to ahigh-resolution inspector system for inspecting a semiconductor sample.This system includes at least one processor and memory that are operablefor performing any combination of the above-described operations.

These and other aspects of the invention are described further belowwith reference to the figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagrammatic representation of a captured candidate defectevent from an optical image and its corresponding SEM image at thedefect event site.

FIG. 2 is a flowchart illustrating an inspection process in accordancewith one embodiment of the present invention.

FIG. 3 is a diagrammatic representation of a detailed learning andcorrelation process for generating modeled optical images from SEMimages in accordance with a specific implementation of the presentembodiment.

FIG. 4 is a diagrammatic representation of an optical inspection systemin accordance with a specific implementation of the present invention

FIG. 5 is a diagrammatic representation of a scanning electronmicroscopy (SEM) system in accordance with one embodiment of the presentinvention.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

In the following description, numerous specific details are set forth inorder to provide a thorough understanding of the present invention. Thepresent invention may be practiced without some of these specificdetails. In other instances, well known process operations have not beendescribed in detail to not unnecessarily obscure the present invention.While the invention will be described in conjunction with the specificembodiments, it will be understood that it is not intended to limit theinvention to the embodiments.

Wafer noise events are real random imperfections on the wafer that arisefrom minor wafer process variation, which may include surface orsubsurface line-edge roughness, pattern edge displacement, filmthickness variation, surface roughness, pattern CD variation, etc. Mostof the wafer noise artifacts have no yield impact and, accordingly, arenot counted as real yield-impacting defects. However, certain noiseartifacts can cause optical scattering and contribute to the opticalnoise in an optical inspection image. Additionally, because of thelimited optical resolution of the inspection tool, the wafer noisedistributed within the optical point spread function can be integratedby the imaging system and form a single merged optically salientsignature in the optical image. When using an aggressive detectionthreshold to catch small defects in advanced nodes, statisticallyspeaking, there is a significant risk that some distributed noisesources in a local wafer area could add up constructively in phase toget a boost in optical intensity and become strong enough to be detectedas a candidate defect. In certain cases, the number of such wafer noisenuisances can be significant, as compared to the number of real defectson the wafer.

Scanning Electron Microscope (SEM) review has been relied upon todifferentiate nuisance from real defect because of the SEM tool'ssuperior resolution with respect to the optical inspection tool.However, an SEM tool is only effective in imaging wafer patterns anddefects at the surface. Unlike the optical inspection tool, the SEM hasa rather limited visibility of sub-surface defects due to the typicallyshort electron mean free path. As a result, the operator of the SEM toolcan often get confused about a candidate defect event that is capturedby the optical tool, but not easily identifiable via an SEM (so-calledSEM-Non-Visual (SNV) event). The operator is then unsure about whetherthe captured event is an important previous process layer defect orsimply wafer noise events.

FIG. 1 is a diagrammatic representation of a candidate defect event 104as captured in an optical image 102 and its corresponding SEM image 122of the same defect event site. For example, the optical image 102corresponds to a 10-20 m field of view (FOV), while the SEM image 122corresponds to a 1-2 m FOV. Although a relatively large defect 104 ispresent in the optical image 102, it is difficult to determine thesource of such defect in the corresponding SEM image 122. For example,the defect event 104 may be formed from multiple noise sources withinthe optical tool's point spread function (PSF) and not distinguishablefrom each other in the optical image 102. For example, two noisesources, including edge roughness 130 and edge displacement 126, asshown in the SEM image 122, may be found in a local pattern area withsize comparable or smaller than the optical tool's PSF. Coincidentally,these two noise sources are optically integrated into a single defectevent 104 in the optical image 102. However, a defect classification orreview process may not be able to readily identify these minor noisediscrepancies 126 and 130 in the SEM image 122 as the root cause of thedefect event 104 in the optical image 102.

Certain embodiments described herein provide quantitative and effectivemethods and systems to correlate a high-resolution (e.g., SEM) image'swafer noise information (e.g., noise events at the wafer surface) with acorresponding optical image's captured defect event that the SEM imagedoes not show as a clearly identifiable defect (e.g., an SNV event).This correlation may be performed by comparing an optical image that hasbeen modeled from an SEM image with a corresponding acquired opticalimage. This correlation allows one to unambiguously identify certainsurface noise events in the SEM image as sources for the correspondingdefect event of the optical image. Conversely, if a correlation betweenthe modelled and acquired optical images does not result inidentification of noise events as sources for a corresponding defectevent, one can then determine that a subsurface event is present. Theinspection mechanisms described herein allow a tool operator orautoclassification process to unambiguously identify whether a candidateevent is due to a defect, surface noise events, or a subsurfacedefect/noise event. Therefore, the correct defect management process canthen be initiated for the wafer and/or subsequent wafers.

Example embodiments described herein sometimes refer to an image, suchas an optical (acquired or simulated) image or a high-resolution image.The term “image” may reference an image acquired directly from thesample, e.g., wafer, a processed or modelled image, or a differenceimage that is obtained by subtracting reference and test/target imagesfrom each other. Each reference image is generally a defect-free versionof the corresponding test image, and each reference image is nominallyidentical to its corresponding test image when such test image isdefect-free. Any of the processes described herein may be implementedwith respect to any of these types of images or any other suitable typeof optical and high-resolution images.

FIG. 2 is a flowchart illustrating an inspection process 200 inaccordance with one embodiment of the present invention. Initially, anoptical inspector may be used to image and detect potential defect sitesat corresponding locations on a wafer in operation 202. In general, theoptical inspector acquires optical images of a wafer (or any suitablesample), compares test and reference optical images to locatedifferences, and determine whether such differences correspond tocandidate defects. Difference events that are above a predeterminedthreshold may be defined as candidate defects, and a correspondingoptical image of such candidate event and its location are then providedby the optical tool. Each optical test image and its correspondingoptical reference image may be in the form of two identically-designeddies or die portions, cells or cell portions, etc.

The candidate defect sites may include actual defects, nuisances, andsub-surface events. In general, the optical inspector may be operable toacquire and provide candidate event locations to a high-resolutioninspector for further review and analysis. The optical images andlocations may be provided directly to the high-resolution inspector ormade available in a separate image database that is accessible by thehigh-resolution inspector.

Next, a high-resolution inspector may be used to image the detectedsites in operation 204. That is, a high-resolution inspectorcaptures/acquires a set of high-resolution images of the potentialdefect sites. The high-resolution inspector generally captures imagescorresponding to a field of view (FOV) that is large enough to contain awafer area that fits within the optical tool's point spread function(PSF), and these high-resolution images will show the actual patternshapes and noise present in the imaged wafer area. The high-resolutioninspector may take any suitable form, such as a scanning electronmicroscope (SEM), Helium ion microscope, atomic force microscope, etc.Embodiments described herein may refer to use of an SEM although anyother suitable high-resolution inspector is also contemplated.

The acquired high-resolution images may then be analyzed to classify thepotential defects into unambiguous and ambiguous events in operation206. The unambiguous events may include bridges, breaks, protrusions,intrusions, CD defects, missing patterns, particles, etc. In general,the high-resolution images may be analyzed by using any suitableclassification process, such as a die-to-die, cell-to-cell,die-to-database inspection, etc. A neural network, algorithm/model, orother learning algorithm may be used for learning defect and/or nuisanceclassifications. Other approaches that use complex computational methodsfor noise-free reference generation can also be used as the detectionalgorithm for the high-resolution images. In addition, a detectionalgorithm that leverages the semiconductor device design file can alsobe used for classifying events in the high-resolution images.

Classification techniques may include thresholding events that havesizes above a predefined threshold as defects. An auto-classificationprocess may also be trained to recognize certain classes of defects,such as bridges, breaks, protrusions, intrusions, CD defects, missingpattern, and particles, based on training images that include suchdefect types. Additionally, knowledge of the design may be used todetermine that a bridge is not intended to be present between two designfeatures. The remaining candidate defects, which cannot be classifiedinto unambiguous events, may then be classified as ambiguous events,which are analyzed further as described herein.

The high-resolution images for the ambiguous events may then beprocessed to generate modeled optical images in operation 208. In oneembodiment, one or more models are used to model an accurate opticalimage from each SEM image. As further described herein the modelingincludes a training process for modeling the near field (NF) opticalimage from the SEM image, as well as an optical tool model for modelingthe final optical image from the NF optical image.

For each ambiguous event, the actual optical image (from the opticalinspector) may then be compared to its corresponding modeled opticalimage to further classify each ambiguous event into surface noise (ordefect) events or subsurface event in operation 210. If the modeledoptical image shows a significantly different signature from theacquired optical images at the candidate defect location, it may bedetermined that the candidate event is a subsurface event since the SEMcannot image subsurface events. A subsurface event may be in the form ofa defect or noise in an underlying layer. If the modeled and acquiredimages are the substantially identical (e.g., having a difference thatis less than a predetermined amount of percentage), the candidate eventmay be flagged as a surface defect or surface noise.

Determining whether to classify a candidate defect as surface orsubsurface events may be accomplished, for example, by measuring andcomparing the signal-to-noise ratio (SNR) in both the differenceacquired image and difference modeled image. In one embodiment, the SNRis calculated by the ratio of the signal intensity at the candidatedefect location against the RMS noise intensity in the surrounding area.If both images have SNR values above a predetermined threshold (forexample ˜3) and a similar signal polarity, the candidate event can beclassified as surface noise. If the SNR in the acquired optical image isabove the threshold while the SNR in the modelled image is significantlybelow the threshold, the candidate event can be classified as asubsurface defect.

A high-resolution image of each classified surface event may then bereviewed to further classify such event as a “real” defect or noise inoperation 211. When the modeled and captured optical images areidentical or substantially identical, one or more SEM features (andcorresponding wafer features) can be identified as contributing to thecandidate defect in either optical image. As further described herein,processing the SEM images to simulate optical images involvesdetermining an offset for aligning the images. This offset can also beused to locate the contributing sources in the SEM image, which can thenbe reviewed to verify if a candidate event is a real defect ornoise/roughness. Thus, one can correlate a location of one or morefeature(s) that cause an optical issue. The correlation results help anoperator to be more certain (and not guess) about source eventlocation(s) for each candidate event. An operator or an automatedprocess, such as an autoclassification process, may be used to make thisfinal classification. A real defect will likely impact yield or devicefunction. In contrast, a nuisance type defect will not impact yield ordevice function. For instance, if a candidate defective event shows noclear indication of the presence of an unambiguous defect in itshigh-resolution image, but its difference acquired optical image can befaithfully reproduced in the difference modeled optical image with asimilar magnitude, position and shape, it may be verified that thecandidate event is indeed a wafer noise nuisance. Otherwise, there is agood possibility the candidate event is attributed to a sub-surfacedefect.

After the candidate events are classified and reviewed, it may then bedetermined whether the wafer passes in operation 212. If the waferpasses, the inspection process may end. The wafer sample may then befurther processed, diced, and formed into packaged devices/systems. Ifthe wafer includes yield-affecting defects, it may be determined thatthe wafer does not pass inspection. Otherwise, if the wafer does notpass, the wafer may be repaired (if possible) or discarded if repair isnot possible in operation 214, after which the inspection process forthe current wafer ends. For repairing the wafer, certainmaterials/structures may be removed or added to the wafer to eliminatethe adverse effects of certain defects. The repaired wafer may then befurther processed, diced, and formed into packaged devices/systems.Repair of the wafer may also include removal of defective dies from thedies that are diced and packaged into devices/systems. Additionally, oralternatively, the process for fabricating a wafer may be adjusted toeliminate defects from occurring on subsequent wafers.

FIG. 3 is a diagrammatic representation of a detailed learning andcorrelation process 300 for generating modeled optical images inaccordance with a specific implementation of the present embodiment.Initially, an optical reference image 302 a and optical test image 302 bmay be input into the training process. Likewise, an SEM reference image306 a and an SEM test image 306 b are input into the training andcorrelation process. In general, each test image corresponds to an areaof the wafer that is being inspected for defects, while eachcorresponding reference image is an image corresponding to a wafer areathat is identical to its corresponding test image when such test imageis defect-free. The reference image may be generated by imaging an areaof the wafer that is designed to be identical to the area of the waferfor the corresponding test area.

In general, a small set of optical and corresponding SEM images, e.g.,five, of the potential defect sites are used in the training process.The training images may include unambiguous images having sites thathave been classified as unambiguous defect or noise events, such asbridges, breaks, or other known defect or noise types, etc. Any suitablenumber of images may be used to train an accurate model for generatingthe modeled optical images from the SEM images. Suitable numbers mayinclude at least 5, 5-10, 10-20, 20-50, or 50-100, depending on factorssuch as image sizes, number of model parameters, etc.

Each set of acquired optical reference and test images 302 a and 302 bmay be subtracted (304) to generate a difference acquired optical image302 c. Of course, these optical images are first aligned beforesubtraction. The resulting difference optical image corresponds to apotential defect or nuisance site generated from the optical inspection.

In the illustrated example, the SEM reference and test images 306 a and306 b are first input into an align, smoothing, and normalizing process308. The alignment operation (for each set of optical and SEM referenceand test images) may include alignment based on any suitable technique,such as moving the reference and test with respect to each other untilan optimized match is accomplished. Before this process, alignment markson the wafer may be used to coarsely align the images with respect toeach other.

Since SEM images are captured with extremely high resolution, e.g., 1nm, the SEM images already provide a best representation of the actualwafer patterns. However, the SEM images will typically have additionalartifacts introduced by the SEM. Smoothing for the SEM images mayinclude removal of noise introduced by the SEM. That is, the images maybe filtered to remove known shot noise and intensity fluctuations thatare known to occur when imaging with the particular SEM tool that isbeing used to image the wafer.

Normalization generally includes normalizing the grayscale of both thereference and test images. For instance, the SEM images may be binarizedto result in pattern contours in the images, like the SEM image 122 ofFIG. 1. That is, the design structures (e.g., 124 and 128) may beassigned a “1” value, while the surrounding field regions (e.g., 132)may be given a “0” value or vice versa.

After processing the SEM images, modeling may then be applied to theresulting SEM images, which best represent the actual wafer area, toobtain accurate optical images of the wafer images, except for theunderlying features which are not imaged by the SEM. First, the SEMimages, along with known pattern characteristics, may be fed into a nearfield model 310 that models SEM near field images from the input. Theknown pattern characteristics, which are typically provided with thewafer being inspected, may include material composition of the scannedwafer layer, including material composition that is present within andoutside the pattern contours of the processed SEM images. For example,the known compositions of the inspected layer may specify a coppermaterial for the patterns (e.g., 124 and 128 of FIG. 1) and oxide forthe field regions (e.g., 132) of the SEM image (e.g., 122). The knowncharacteristics for the imaged wafer portion may also include known orapproximated compositions, topography, and thicknesses of surface andunderlying layers and patterns.

The NF model starts with the smoothed and normalized SEM images, whichrepresent the actual wafer pattern, and simulates light reflecting fromthe different materials of the different portions at the surface of thewafer pattern as represented in the processed SEM images. That is, thenear field modeling simulates light reflected from specified layermaterial within and outside the pattern contours as represented in thecorresponding SEM image to then simulate an NF image near the surface ofthe wafer pattern as represented by the processed SEM image. The nearfield images are simulated at a point proximate to the sample and priorto the reflected light passing through the optical tool's imaging pathonto the detector. Any suitable NF model may be used, such as aKirchhoff model, which includes parameters that can be calibrated inorder to give the best match between the final rendered and acquiredoptical.

The resulting near field image may then be input to an optical systemmodel 312 that models the light on the detector of the optical toolafter it passes through the optics. In example embodiments, the opticalsystem model may comprise one of more of the following elements: apartial coherent model (PCM) to model a partial coherent imaging system,a source spectral model to model an incoherent source with finitebandwidth, a polarization model to model different optical polarizationstates, etc. In general, the optical system model uses parameters andcharacteristics of the optical tool being used to collect actual opticalimages in this embodiment. Any suitable parameters and characteristicsthat have values or settings that affect the optical electromagneticwaves from the NF image through the collection path and onto thedetector may be used. By way of examples, these parameters may includemetrics for wavelength, focus, numerical aperture (NA), shape ofillumination and imaging apertures, and aberrations present in theoptical tool.

These parameters are input into the optical model to transform the NFimage into an optical intensity image, which is what would be imaged bythe optical tool starting with the NF image. That is, thehigh-resolution SEM images are translated into more accurate opticalimages that can be then directly compared to the corresponding acquiredoptical images.

However, optical images that have an optimal accuracy are obtained bytraining the NF model (the optical model is already well-known and doesnot require training). The training, which uses only a finite number ofSEM and optical images, may be performed once to finalize the NF model.After such training, additional SEM images may then be transformed intofinal optical images without any further training.

Returning to the training example of FIG. 3, the training continues byinputting the optical images that are output from the optical systemmodel 312 to a down-sampling process 314. This process 314 is operableto change the resolution and size of the modeled optical images into asame resolution and size as the optical tool. For example, theresolution of the modelled optical images is changed from 1 nm, which isthe pixel size of the high-resolution SEM tool, to the optical tool'spixel size of 50 nm. In one implementation, the down-sampling process314 may remove a certain distribution of pixels across the modeledoptical images.

A down-sampled optical image, along with its corresponding acquiredoptical image, may then be fed into an alignment process 315. In theillustrated example, a test modeled optical image and its correspondingtest acquired optical image are aligned with each other. Alternatively,a reference modeled image and its corresponding reference acquiredoptical image may be aligned. Alternatively, a difference modeled andacquired image may be aligned to determine an offset. However, since thetest images include the potential defect site, the test images arepreferably used. Any suitable alignment process may be implemented. Forinstance, the images may be shifted with respect to each other untilthere is a maximum match. The resulting offset may then be fed into ashifting process 316 for shifting the modeled optical images so theyalign with the acquired optical image. Alternatively, but lesspreferably, the shifting process may be performed on the acquiredoptical images.

In general, when a candidate defect is found in an actual optical image,the position (X₀, Y₀) of such candidate defect is provided for locatingsuch candidate defect on the SEM tool. That is, the optical tool recordsthe position of each candidate defect, and the SEM tool is ideallyoperable to center (with or without the operator's help) on suchprovided position. However, due to the stage positioning accuracylimitation of the optical tool and/or the SEM tool, the defectpositioning (X₀, Y₀) accuracy may be subpar. For instance, the positionerror for positioning the candidate defect on the SEM tool can be ashigh as 500 nm-1 um. Additionally, since some defect locations can beclear in the optical image, but not in the SEM image, it may bedifficult to determine which portion of the SEM image corresponds to theoptical defect. Certain embodiments of the present invention allowidentification of the defect source location(s) in the SEM image bymodeling a corresponding optical image, which is then aligned with thecaptured optical image to provide a shift error. This shift error (oroffset) can then be used to locate the correct candidate defect positionin the corresponding SEM image.

After an offset has been determined and a shift occurs, the test andreference modeled optical images may then be subtracted (318) to form adifference modeled optical image 306 c. The other training SEM imagesand their corresponding acquired optical images can also be similarlyprocessed to achieve correlation results for the entire set of trainingimages. If the correlation results are not optimized, an NF parametersadjustment process 322 alters the NF parameters. The modeling operationsare repeated for the set of training SEM images to form a new set ofmodeled optical images. The NF parameters continue to be adjusted untilthe modeled optical images are optimally correlated with the acquiredoptical images. Optimum correlation may be defined in any suitablemanner. For example, an image intensity correlation with R²>90% can bedefined as optimum after the calibration. Other optimum correlationspecifications may include RMS value of the difference between modeledoptical image and acquired optical image, etc.

The resulting final NF parameters may then be used for processing theentire set of SEM images having ambiguous events. Each differencemodeled optical image 306 c, along with the corresponding differenceacquired optical image 302 c may be input to an output correlationprocess 320. Additionally, each resulting difference modeled opticalimage may then be correlated with its corresponding difference acquiredoptical image to further classify the ambiguous events into surfacenoise, a surface defect, or a subsurface event (e.g., 210).

Any suitable optical inspection tool may be implemented for detectingand imaging candidate events as described herein. Certain inspector toolembodiments provide flexible wavelength selection in order to cover avast range of wafer material properties and structures. Additionally,the inspection tool may be operable to provide shorter wavelengths andmay include flexible polarization selection in order to obtain the bestsignal. The inspection system may also be operable to collect differentforms of information in one scan to improve the inspection throughput,defect classification, nuisance suppression.

In general, applicable optical inspection tools for implementation oftechniques of the present invention may include at least one lightsource for generating an incident light beam at selected wavelengths todetect defects in different material types and structure types. Such aninspection may also include illumination optics for directing theincident beam to the area-of-interest, collection optics for directingan output beam that is emitted from the area-of-interest in response tothe incident beam, a sensor for detecting an output beam and generatingan image or signal from the detected output beam, and a controller forcontrolling the components of the inspection tool and facilitatingdefect detection. Example tools include the 2930, 2935, and 3900inspection system available from KLA Corp. of Milpitas, Calif.

In one embodiment, an inspection tool includes illumination andcollection modules for generating and collecting light at a wide rangeof wavelengths (above 190 nm-950 nm). The inspector tool may alsoprovide polarization options for parallel and perpendicular e-field anda set of sub-band wavelength filters for applying across the wavelengthrange for each of the long and short wavelength paths.

FIG. 4 is a diagrammatic representation of an optical inspection system400 in accordance with a specific implementation of the presentinvention. As shown, the system 400 may include a broadband light source(e.g., illuminator 404) which generates illumination light 405. Examplesof light sources include a laser-driven light source, a high-powerplasma light source, a transillumination light source (e.g., halogen orXe lamp), a filtered lamp, LED light sources, etc. The inspection systemmay include any suitable number and type of additional light sources,besides broadband light sources.

The incident beam from the light source may generally pass through anynumber and type of lenses which serve to relay (e.g., shape, focus oradjust focus offset, filter/select wavelengths, filter/selectpolarization states, resize, magnify, reduce distortion, etc.) the beamtowards a sample 434.

The illuminator 404 may include any suitable optical elements forgenerating an incident beam having selected wavelength ranges. Forexample, the illuminator 404 may include a filter wheel 401 withselectable bandpass filters that are individually inserted (or rotated)into the illumination path to change the wavelength of the illuminationbeam. Generally, each inspection wavelength range may be selected basedon optimization of its illumination and collection pupil apertureshapes, polarization of the incident and collection path, magnification,pixel size, or any combination thereof.

The illuminator may also include one or more additional spectral filters(e.g., 403) that may be used to further define the spectrum of theincident beam. For example, each spectral filter can further be used tooptimize the sensitivity for the defects that are intended to becaptured. A separate polarizing filter 406 can also be selectivelypositioned in the incident beam path to further optimize the inspectionsensitivity for different wavelength ranges.

A pupil relay (not shown) may also be used to reimage the incident lightand focus the pupil onto the system pupil at the objective lens 432. A50-50 beam splitter 428 may be used to send the light to the objectivelens 432. The 50-50 beam splitter 428 may also be arranged to send lightreflected or scattered from the sample toward collection optics. A pupilthat is conjugate to the system pupil (located at the objective lens)may be used in the incident beam path. Each pupil or aperture can have aspecific shape to obscure parts of the light path to maximize the signalfor the selected wavelength range.

The objective lens 432 is preferably optimized for all selectablewavelengths that are used for defect detection. For instance, theobjective 432 has a composition, including lens coatings, andarrangement for correction of color aberration. In an alternativeembodiment, the objective lens 432 may be an all reflective objective orrefractive or a combination (catadioptric) configuration.

The resulting output beam reflected or scattered from the sample maythen be received by another dichroic beam splitter 437, which may bearranged to insert an autofocus into the objective lens 432 viaauto-focus system 435. The autofocus beam may have a wavelength that isseparated from the different inspection bands. The wavelength for theautofocus can be varied as long as it is not in the inspection wavebandsfor any of the selected inspection wavelength ranges. Cost andavailability of components can affect where the auto-focus insertion islocated. The autofocus wavelength band may be 40 nm or less to minimizefocus plane change due to wafer material response. For instance, theauto-focus system 435 may use an LED light source. The dichroic beamsplitter 437 may be arranged to reflect the autofocus waveband andtransmit all light above and below that region. The 50-50 beam splitter428 can also be configured to pass the autofocus light with highefficiency (e.g., by use of a coating). This element may improve thelight efficiency of the auto-focus by nearly 4×.

If the autofocus wavelength is much higher than the selectableinspection wavelength ranges, the autofocus beam will be affected bydifferent thermally induced focus change than the inspection imagingsystem. The system may include mechanisms to provide feedback on thefocus change due to environment (temperature, pressure), lens heating,etc. By way of examples, they auto-focus system 435 may include feedbackmechanisms in the form of temperature and pressure sensors and acalibration wafer installed on the side of the wafer chuck forevaluating the focal plane change. Based on feedback, the auto-focussystem 435 may adjust one or more of the lens elements (such as bymoving lenses to form an adjustable air gap) to introduce focuscorrection or may adjust the stage (and sample 434 thereon) z positionvia one or more drivers 408. The frequency of the correction can also bedetermined based on such feedback.

The system 400 may operate in any scanning mode known in the art. Forexample, the system 400 may operate in a swathing mode when theillumination beam scans across the surface of the sample 434. In thisregard, the system 400 may scan an illumination beam across the sample434, while the sample is moving, with the direction of scanning beingnominally perpendicular to the direction of the sample motion.

The output beam may be directed and shaped by any suitable number andtype of collection optics, such as pupil relay (lens group 440) and,mirror 438, a polarizer 407, aperture 409, and optics elements 410 and412 for zooming and focusing the output beam onto sensor 454. By way ofexample, the sensor 454 may be in the form of a CCD (charge coupleddevice) or TDI (time delay integration) detector, photomultiplier tube(PMT), or other sensor.

The pupil relay 440 may be designed to form an image of the system pupil(at the objective lens 432) for the purpose of inserting specificapertures (409) in the collection path to optimize the inspectionsensitivity for each wavelength range. Different aperture settings maybe selected to achieve different angles of incidence on the sample. Apolarizing filter (405 or 407) may be positioned in the illumination orcollection path for the purpose of also optimizing the inspectionsensitivity.

The sample 434 may be disposed on sample stage 414 configured to supportthe sample 434 during scanning. In some embodiments, the sample stage414 is an actuatable stage. For example, the sample stage 414 mayinclude, but is not limited to, one or more translational stagessuitable for selectably translating the sample 434 along one or morelinear directions (e.g., x-direction, y-direction and/or z-direction).By way of another example, the sample stage 414 may include, but is notlimited to, one or more rotational stages suitable for selectablyrotating the sample 434 along a rotational direction. By way of anotherexample, the sample stage 414 may include, but is not limited to, arotational stage and a translational stage suitable for selectablytranslating the sample along a linear direction and/or rotating thesample 434 along a rotational direction.

The system may also include a controller or computer system (e.g., 490).For instance, the signals captured by each detector can be processed bycontroller 490, which may include a signal processing device having ananalog-to-digital converter configured to convert analog signals fromeach sensor into digital signals for processing. The controller may beconfigured to analyze intensity, images, phase, and/or othercharacteristics of the sensed light beam. The controller may beconfigured (e.g., with programming instructions) to provide a userinterface (e.g., on a computer screen) for displaying resultant testimages and other inspection characteristics as described further herein.The controller may also include one or more input devices (e.g., akeyboard, mouse, joystick) for providing user input, such as changingwavelength, polarization, or aperture configuration, viewing detectionresults data or images, setting up an inspection tool recipe.

Techniques of the present invention may be implemented in any suitablecombination of hardware and/or software, such as controller 490. Thecontroller typically has one or more processors coupled to input/outputports, and one or more memories via appropriate buses or othercommunication mechanisms.

The controller may be generally configured to control various componentsof the inspection system. For instance, the controller may controlselective activation of the illumination source, the illumination oroutput aperture settings, wavelength band, focus offset setting,polarization settings, stage and beam steering, etc. The controller mayalso be configured to receive the image or signal generated by eachdetector and analyze the resulting image or signal to determine whethercandidate events (defects/nuisances) are present on the sample, providelocations of candidate events, characterize defects present on thesample, or otherwise characterize the sample. For example, thecontroller may include a processor, memory, and other computerperipherals that are programmed to implement instructions of the methodembodiments of the present invention.

Because such information and program instructions may be implemented ona specially configured computer system, such a system includes programinstructions/computer code for performing various operations describedherein that can be stored on a computer readable media. Examples ofmachine-readable media include, but are not limited to, magnetic mediasuch as hard disks, floppy disks, and magnetic tape; optical media suchas CD-ROM disks; magneto-optical media such as optical disks; andhardware devices that are specially configured to store and performprogram instructions, such as read-only memory devices (ROM) and randomaccess memory (RAM). Examples of program instructions include bothmachine code, such as produced by a compiler, and files containinghigher level code that may be executed by the computer using aninterpreter.

Any suitable combination of hardware and/or software may be used toimplement a high-resolution inspector. FIG. 5 is a diagrammaticrepresentation of a scanning electron microscopy (SEM) system 500 inaccordance with one embodiment of the present invention. The system 500may be configured to scan each candidate event of a sample 506 such as,but not limited to, a wafer (e.g., semiconductor wafer) having two ormore layers formed thereon with an electron beam 504 to capture SEMimages.

The system 500 may operate in any scanning mode known in the art. Forexample, the system 500 may operate in a swathing mode when scanning anelectron beam 504 across a candidate defect site of the sample 506. Inthis regard, the system 500 may scan an electron beam 504 across thesample 506, while the sample is moving, with the direction of scanningbeing nominally perpendicular to the direction of the sample motion. Byway of another example, the system 500 may operate in a step-and-scanmode when scanning an electron beam 504 across the surface of the sample506. In this regard, the system 500 may scan an electron beam 504 acrossthe sample 506, which is nominally stationary when the beam 504 is beingscanned.

The system 500 may include an electron beam source 502 for generatingone or more electron beams 504. The electron beam source 502 may includeany electron beam source known in the art. For example, the electronbeam source 502 may include, but is not limited to, one or more electronguns. In some embodiments, a computing system 524 or controller may becommunicatively coupled to the electron beam source 502. The computingsystem 524 may be configured to adjust one or more electron sourceparameters via a control signal to the electron beam source 502. Forexample, the computing system 524 may be configured to vary the beamcurrent for the electron beam 504 emitted by source 502 via a controlsignal transmitted to control circuitry of the electron beam source 502.

The sample 506 may be disposed on a sample stage 508 configured tosupport the sample 506 during scanning. In some embodiments, the samplestage 508 is an actuatable stage. For example, the sample stage 508 mayinclude, but is not limited to, one or more translational stagessuitable for selectably translating the sample 506 along one or morelinear directions (e.g., x-direction, y-direction and/or z-direction).By way of another example, the sample stage 508 may include, but is notlimited to, one or more rotational stages suitable for selectablyrotating the sample 506 along a rotational direction. By way of anotherexample, the sample stage 508 may include, but is not limited to, arotational stage and a translational stage suitable for selectablytranslating the sample along a linear direction and/or rotating thesample 506 along a rotational direction.

In some embodiments, the computing system 524 or controller iscommunicatively coupled to the sample stage 508. The computing system524 may be configured to adjust one or more stage parameters via acontrol signal transmitted to the sample stage 508. The computing system524 may be configured to vary the sample scanning speed and/or controlthe scan direction via a control signal transmitted to control circuitryof the sample stage 508. For example, the computing system 524 may beconfigured to vary the speed and/or control the direction with whichsample 506 is linearly translated (e.g., x-direction or y-direction)relative to the electron beam 504. As discussed in further detailherein, the sample 506 may be scanned in a tilted orientation relativeto feature placement (e.g., perpendicular or tilted otherwise withrespect to a longitudinal axis of the pattern lines) of targetstructures forming an overlay metrology target or mark on the sample506.

The system 500 may further include a set of electron-optic elements 510.The set of electron-optics may include any suitable elements known inthe art suitable for focusing and/or directing the electron beam 504onto a selected portion of the sample 506. In one embodiment, the set ofelectron-optics elements includes one or more electron-optic lenses. Forexample, the electron-optic lenses may include, but are not limited to,one or more condenser lenses 512 for collecting electrons from theelectron beam source. By way of another example, the electron-opticlenses may include, but are not limited to, one or more objective lenses514 for focusing the electron beam 504 onto a selected region of thesample 506. In some embodiments, the electron beam 504 may be directedto the sample 506 at a controlled angle to the sample. Because a wafersystem of coordinates does not necessarily coincide with an SEM systemof coordinates, controlling a fine scan angle may improve matchingbetween the coordinate systems and significantly contribute to samplingperformance/accuracy.

In some embodiments, the set of electron-optics elements includes one ormore electron beam scanning elements 516. For example, the one or moreelectron beam scanning elements 516 may include, but are not limited to,one or more scanning coils or deflectors suitable for controlling aposition of the beam relative to the surface of the sample 506. In thisregard, the one or more scanning elements 516 may be utilized to scanthe electron beam 504 across the sample 506 in a selected scan directionor pattern. For example, the sample 506 may be scanned in tilted orperpendicular bidirectional scans relative to feature placement (e.g.,at bidirectional directions and angled with respect to target lines) oftarget structures forming an overlay metrology target or mark on thesample 506. The computing system 524 or controller may becommunicatively coupled to one or more of the electron-optic elements510, such as the one or more scanning elements 516. Accordingly, thecomputing system may be configured to adjust one or more electron-opticparameters and/or control the scan direction via a control signaltransmitted to the one or more communicatively coupled electron-opticelements 510.

The system 500 may further include a detector assembly 518 configured toreceive electrons 517 from the sample 506. In some embodiments, thedetector assembly 518 includes an electron collector 520 (e.g.,secondary electron collector). The detector assembly may further includean energy filter based, for example, on retarding field principle. Inthis regard, the energy filter may be configured to stop low energysecondary electrons while passing high energy secondary electrons (i.e.,backscattered electrons). If the energy filter is not activated, allsecondary electrons are detected according to collection efficiency ofthe detection system. By subtracting high energy electron image fromoverall electron image, low energy secondary electron image can beobtained. The detector assembly 518 may further include a detector 522(e.g., scintillating element and PMT detector 522) for detectingelectrons from the sample surface (e.g., secondary electrons). In someembodiments, the detection system 522 may include several electrondetectors, such as, for example, one or more Bright Field (BF) detectors521 and one or more Dark Field (DF) detectors 523. In some embodiments,there may be from 2 to 8 (or even more) DF detectors 523. The BFdetector 521 detects electrons with low (according to wafer normal)emission angles, while DF detectors 523 provide information carried bythe electrons with higher emission angles. In some embodiments, thedetector 522 of the detector assembly 518 includes a light detector. Forexample, the anode of a PMT detector of the detector 522 may include aphosphor anode, which is energized by the cascaded electrons of the PMTdetector absorbed by the anode and may subsequently emit light. In turn,the light detector may collect light emitted by the phosphor anode inorder to image the sample 506. The light detector may include any lightdetector known in the art, such as, but not limited to, a CCD detectoror a CCD-TDI detector. The system 500 may include additional/alternativedetector types such as, but not limited to, Everhart-Thornley typedetectors. Moreover, the embodiments described herein are applicable tosingle detector and multiple-detector arrangements.

In some embodiments, the computing system 524 or controller iscommunicatively coupled to the detector assembly 518. The computingsystem 524 may be configured to adjust one or more image formingparameters via a control signal transmitted to the detector assembly518. For example, the computing system may be configured to adjust theextraction voltage or the extraction field strength for the secondaryelectrons. Those skilled in the art will appreciate that “the computingsystem 524” may include one or more computing systems or controllers,such as one or more processors configured to execute one or moreinstruction sets embedded in program instructions stored by at least onenon-transitory signal bearing medium. The computing system 524 maycontrol various scanning or sampling parameters such as, but not limitedto, those described herein.

While the foregoing description focused on the detector assembly 518 inthe context of the collection of secondary electrons, this should not beinterpreted as a limitation on the present invention. It is recognizedherein that the detector assembly 518 may include any device orcombination of devices known in the art for characterizing a samplesurface or bulk with an electron beam 504. For example, the detectorassembly 518 may include any particle detector known in the artconfigured to collect backscattered electrons, Auger electrons,transmitted electrons or photons (e.g., x-rays emitted by surface inresponse to incident electrons). In some embodiments, the detectedelectrons are differentiated (e.g., secondary electrons vs.backscattered electrons) based upon the energy levels and/or emissionangles of the detected electrons, and by subtracting high energyelectron image from overall electron image, low energy secondaryelectron image can be obtained.

The computing system 524 may be configured to receive and/or acquiredata or information (e.g., detected signals/images, statistical results,reference or calibration data, training data, models, extracted featuresor transformation results, transformed datasets, curve fittings,qualitative and quantitative results, etc.) to and from other systems bya transmission medium that may include wireline and/or wirelessportions. In this manner, the transmission medium may serve as a datalink between the computing system 524 and other systems (e.g., memoryon-board metrology system, external memory, reference measurementsource, or other external systems). For example, computing system 524may be configured to receive measurement, imaging, and location datafrom a storage medium (e.g., internal or external memory) via a datalink. For instance, results obtained using the detection system may bestored in a permanent or semi-permanent memory device (e.g., internal orexternal memory). In this regard, the results may be imported fromon-board memory or from an external memory system. Moreover, thecomputing system 524 may send data to other systems via a transmissionmedium. For instance, qualitative and/or quantitative results (e.g.,model parameters, models, classifications of candidate events, acquiredand modelled images, etc.), determined by computing system 524 may becommunicated and stored in an external memory. In this regard, analysisresults may be exported to another system.

Computing system 524 may include, but is not limited to, a personalcomputer system, mainframe computer system, workstation, image computer,parallel processor, or any other device known in the art. In general,the term “computing system” may be broadly defined to encompass anydevice having one or more processors, which execute instructions from amemory medium. Program instructions may be stored in a computer readablemedium (e.g., memory). Exemplary computer-readable media includeread-only memory, a random-access memory, a magnetic or optical disk, ora magnetic tape.

In other embodiments, a SEM having a column array system may be used forcapturing, processing, and analyzing high-resolution images. Severalexample systems are described further in U.S. patent application Ser.No. 16/272,905, entitled “ULTRA-HIGH SENSITIVITY HYBRID INSPECTION WITHFULL WAFER COVERAGE CAPABILITY”, by Grace Chen et al., which applicationis incorporated herein by reference for all purposes. For instance, theSEM inspector includes a linear column array and a swathingcontinuously-moving precision stage. In one example, each probe in thelinear probe array is miniaturized so the array can span the wafer. Forinstance, the probes can be formed from microelectromechanical system(MEMS) technology. Of course, other types of probes can be implemented.

In any of the systems and techniques described herein, computationalalgorithms are usually optimized for inspection or metrologyapplications with one or more approaches being used such as design andimplementation of computational hardware, parallelization, distributionof computation, load-balancing, multi-service support, dynamic loadoptimization, etc. Different implementations of algorithms can be donein firmware, software, FPGA, programmable optics components, etc.

Certain embodiments of the present invention presented here generallyaddress the field of semiconductor metrology and process control, andare not limited to the hardware, algorithm/software implementations andarchitectures, and use cases summarized above.

Although the foregoing invention has been described in some detail forpurposes of clarity of understanding, it will be apparent that certainchanges and modifications may be practiced within the scope of theappended claims. It should be noted that there are many alternative waysof implementing the processes, systems, and apparatus of the presentinvention. Accordingly, the present embodiments are to be considered asillustrative and not restrictive, and the invention is not to be limitedto the details given herein.

What is claimed is:
 1. A method of inspecting a semiconductor sample,the method comprising: providing a plurality of locations correspondingto a plurality of candidate defect events on a semiconductor sample froman optical inspector operable to acquire a plurality of acquired opticalimages from which such candidate defect events are detected at theircorresponding locations across the sample; acquiring high-resolutionimages from a high-resolution inspector operable to acquire suchhigh-resolution images of the plurality of candidate defect events attheir corresponding locations on the sample; correlating each of a firstset of modelled optical images, which have been modeled from a firstsubset of the acquired high-resolution images, with a corresponding oneof a first set of the acquired optical images, to identify a pluralityof surface noise events, as shown in the first set of high-resolutionimages, as sources for the corresponding candidate defect events in thefirst set of acquired optical images, wherein the correlating of each ofthe first set of modelled optical images results in identification ofthe surface noise events as sources if the corresponding modelled andacquired optical images are substantially identical; and correlatingeach of a second set of modelled optical images, which have been modeledfrom a second subset of the acquired high-resolution images, with acorresponding one of a second set of the acquired optical images so thatnoise events are not identified as sources and, instead, subsurfaceevents are identified as sources for the corresponding candidate defectevents in the second set of acquired optical images.
 2. The method ofclaim 1, wherein each candidate event represents a surface defect event,one or more noise events, or a subsurface event present on the sample.3. The method of claim 1, further comprising prior to correlating thefirst and second set of modelled images with their corresponding firstand second sets of acquired optical images, analyzing thehigh-resolution images to classify the candidate events into ambiguousand unambiguous events, wherein each high-resolution image in the firstand second set of high-resolution images is associated with a classifiedambiguous event, wherein each unambiguous event is a bridge, break,protrusion, intrusion, or other known defect types, wherein theambiguous events were unclassifiable as a known defect type.
 4. Themethod of claim 3, further comprising: training a near field (NF) modelto model a plurality of NF images from corresponding acquiredhigh-resolution images, wherein the NF model is trained with a set oftraining high-resolution images and acquired optical images thatcorrespond to unambiguous and classified events; modeling the first andsecond sets of modeled optical images by modeling a plurality ofcorresponding NF images from the first and second sets of acquiredhigh-resolution images using the trained NF model and modeling the firstand second sets of modeled optical images from the corresponding NFimages using an optical tool model for the optical inspector.
 5. Themethod of claim 4, wherein the NF model is configured to simulate lightreflected and scattered, with a plurality of light characteristicparameters, from a wafer pattern, having a set of pattern characteristicparameters, that is represented in the corresponding high-resolutionimages, wherein the NF model is trained by: inputting the traininghigh-resolution images into the NF model to model corresponding trainingNF images based on the light and pattern characteristic metrics;inputting the training NF images that were modelled from the traininghigh-resolution images into the optical model to model correspondingtraining modeled optical images; correlating the training modeledoptical images with their corresponding acquired optical images; andadjusting the light and pattern characteristic parameters and repeatingthe operations for inputting the training high-resolution images intothe NF model, inputting the training NF images into the optical model,and correlating the training modeled optical images until suchcorrelating operation results in a maximum correlation between thetraining modeled optical images and their corresponding acquired opticalimages.
 6. The method of claim 4, wherein modeling the first and secondsets of modeled optical images is performed with respect to the firstand second set of high-resolution images after they have been smoothedto remove noise introduced by the high-resolution inspector and havebeen binarized by a normalization process, and the correlating of eachof the first and second set of modelled optical images withcorresponding acquired optical images is performed after such first andsecond modeled optical images are down-sampled so that their resolutionand/or size are the same as a resolution and size of the correspondingacquired optical images.
 7. The method of claim 4, further comprising:shifting each modelled optical image with respect to its correspondingacquired image by an offset that is determined by aligning a one of thetraining modeled optical images with a corresponding one of the acquiredimages, wherein the shifting results in one or more noise events in ahigh-resolution image from the first set of high-resolution images beingaccurately correlated with a corresponding candidate event in thecorresponding acquired optical image.
 8. The method of claim 3, furthercomprising: after correlating the first and second set of modelledimages, determining whether the sample is to be processed further withor without repair or discarded based on review of the high-resolutionimages, the classified unambiguous events, and the identified noise andsubsurface events if any.
 9. The method of claim 1, wherein thehigh-resolution inspector is a scanning electron (SEM) microscope.
 10. Ahigh-resolution inspector system for inspecting a semiconductor sample,comprising at least one processor and memory that are operable forperforming the following operations: providing a plurality of locationscorresponding to a plurality of candidate defect events on asemiconductor sample from an optical inspector operable to acquire aplurality of acquired optical images from which such candidate defectevents are detected at their corresponding locations across the sample;acquiring high-resolution images of the plurality of candidate defectevents at their corresponding locations on the sample; correlating eachof a first set of modelled optical images, which have been modeled froma first subset of the acquired high-resolution images, with acorresponding one of a first set of the acquired optical images, toidentify a plurality of surface noise events, as shown in the first setof high-resolution images, as sources for the corresponding candidatedefect events in the first set of acquired optical images, wherein thecorrelating of each of the first set of modelled optical images resultsin identification of the surface noise events as sources if thecorresponding modelled and acquired optical images are substantiallyidentical; and correlating each of a second set of modelled opticalimages, which have been modeled from a second subset of the acquiredhigh-resolution images, with a corresponding one of a second set of theacquired optical images so that noise events are not identified assources and, instead, subsurface events are identified as sources forthe corresponding candidate defect events in the second set of acquiredoptical images.
 11. The system of claim 10, wherein each candidate eventrepresents a surface defect event, one or more noise events, or asubsurface event present on the sample.
 12. The system of claim 10,wherein the at least one processor and memory are further operable for,prior to correlating the first and second set of modelled images withtheir corresponding first and second sets of acquired optical images,analyzing the high-resolution images to classify the candidate eventsinto ambiguous and unambiguous events, wherein each high-resolutionimage in the first and second set of high-resolution images isassociated with a classified ambiguous event, wherein each unambiguousevent is a bridge, break, protrusion, intrusion, or other known defecttypes, wherein the ambiguous events were unclassifiable as a knowndefect type.
 13. The system of claim 12, wherein the at least oneprocessor and memory are further operable for: training a near field(NF) model to model a plurality of NF images from corresponding acquiredhigh-resolution images, wherein the NF model is trained with a set oftraining high-resolution images that correspond to unambiguous andclassified events; modeling the first and second sets of modeled opticalimages by modeling a plurality of corresponding NF images from the firstand second sets of acquired high-resolution images using the trained NFmodel and modeling the first and second sets of modeled optical imagesfrom the corresponding NF images using an optical tool model for theoptical inspector.
 14. The system of claim 13, wherein the NF model isconfigured to simulate light reflected and scattered, with a pluralityof light characteristic parameters, from a wafer pattern, having a setof pattern characteristic parameters, that is represented in thecorresponding high-resolution images, wherein the NF model is trainedby: inputting the training high-resolution images into the NF model tomodel corresponding training NF images based on the light and patterncharacteristic metrics; inputting the training NF images that weremodelled from the training high-resolution images into the optical modelto model corresponding training modeled optical images; correlating thetraining modeled optical images with their corresponding acquiredoptical images; and adjusting the light and pattern characteristicparameters and repeating the operations for inputting the traininghigh-resolution images into the NF model, inputting the training NFimages into the optical model, and correlating the training modeledoptical images until such correlating operation results in a maximumcorrelation between the training modeled optical images and theircorresponding acquired optical images.
 15. The system of claim 13,wherein modeling the first and second sets of modeled optical images isperformed with respect to the first and second set of high-resolutionimages after they have been smoothed to remove noise introduced by thehigh-resolution inspector and have been binarized by a normalizationprocess, and the correlating of each of the first and second set ofmodelled optical images with corresponding acquired optical images isperformed after such first and second modeled optical images aredown-sampled so that their resolution and/or size are the same as aresolution and size of the corresponding acquired optical images. 16.The system of claim 13, wherein the at least one processor and memoryare further operable for: shifting each modelled optical image withrespect to its corresponding acquired image by an offset that isdetermined by aligning a one of the training modeled optical images witha corresponding one of the acquired images, wherein the shifting resultsin one or more noise events in a high-resolution image from the firstset of high-resolution images being accurately correlated with acorresponding candidate event in the corresponding acquired opticalimage.
 17. The system of claim 12, wherein the at least one processorand memory are further operable for: after correlating the first andsecond set of modelled images, determining whether the sample is to beprocessed further with or without repair or discarded based on review ofthe high-resolution images, the classified unambiguous events, and theidentified noise and subsurface events if any.
 18. The system of claim10 comprising a SEM system.